text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * ... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
# post-processing
image = self.movq.decode(latents, force_not_quantize=Tr... | 75 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py |
class KandinskyPipeline(DiffusionPipeline):
"""
Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a part... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
movq: VQModel,
):
super().__init__()
self.register_modules(
text_encoder=text_e... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if ... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated ... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
uncond_t... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, ... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
return prompt_embeds, text_encoder_hidden_states, text_mask
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image_embeds: Union[torch.Tensor, List[torch.Tensor]],
negative_image_embeds: Union[torch.Tensor, Li... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
negative_im... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as de... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`boo... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": imag... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * ... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
# post-processing
image = self.movq.decode(latents, force_not_quantize=Tr... | 76 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py |
class MCLIPConfig(XLMRobertaConfig):
model_type = "M-CLIP"
def __init__(self, transformerDimSize=1024, imageDimSize=768, **kwargs):
self.transformerDimensions = transformerDimSize
self.numDims = imageDimSize
super().__init__(**kwargs) | 77 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py |
class MultilingualCLIP(PreTrainedModel):
config_class = MCLIPConfig
def __init__(self, config, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.transformer = XLMRobertaModel(config)
self.LinearTransformation = torch.nn.Linear(
in_features=config.transformerDi... | 78 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py |
class KandinskyCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, ... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
une... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
""" | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
_load_connected_pipes = True
model_cpu_offload_seq = "text_encoder->unet->movq->prior_prior->prior_image_encoder->prior_text_encoder"
_exclude_from_cpu_offload = ["prior_prior"]
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DC... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Of... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.se... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
@torch.no_grad()
@replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_promp... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Gui... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually ... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
outputs = self.decoder_pipe(
prompt=prompt,
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=gui... | 79 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
class KandinskyImg2ImgCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or ... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
une... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
""" | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
_load_connected_pipes = True
model_cpu_offload_seq = "prior_text_encoder->prior_image_encoder->prior_prior->" "text_encoder->unet->movq"
_exclude_from_cpu_offload = ["prior_prior"]
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Of... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.se... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
@torch.no_grad()
@replace_example_docstring(IMAGE2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
negative_prompt: Optional[Union[str, List[str]]] = None,
... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, or tensor representing an image batch,... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Gui... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually ... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
if (
isinstance(image, (list, tuple))
and len(image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(image) == 0
):
image = (image_embeds.shape[0] // len(image)) * image
outputs = self.decoder_pipe(
prompt=prompt,
image=ima... | 80 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
class KandinskyInpaintCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
une... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
""" | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
_load_connected_pipes = True
model_cpu_offload_seq = "prior_text_encoder->prior_image_encoder->prior_prior->text_encoder->unet->movq"
_exclude_from_cpu_offload = ["prior_prior"]
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DC... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Of... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.se... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
@torch.no_grad()
@replace_example_docstring(INPAINT_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
mask_image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], ... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Function invoked when calling the pipeline for generation. | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, or tensor representing an image batch,... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The num... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually ... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callb... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
if (
isinstance(image, (list, tuple))
and len(image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(image) == 0
):
image = (image_embeds.shape[0] // len(image)) * image
if (
isinstance(mask_image, (list, tuple))
and len(ma... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
outputs = self.decoder_pipe(
prompt=prompt,
image=image,
mask_image=mask_image,
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
width=width,
height=height,
num_inference_steps=num_inference_steps,
... | 81 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py |
class StableDiffusion3ControlNetPipeline(
DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin
):
r"""
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatch... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
as its dimension.
text_encoder_2 ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_3 (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
controlnet ([`SD3Cont... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
_optional_components = ["image_encoder", "feature_extractor"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"] | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
def __init__(
self,
transformer: SD3Transformer2DModel,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKL,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
text_encoder_2: CLIPTextModelWithProjection,
tokenizer_2: CLIPToken... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
hasattr(controlnet_model.config, "use_pos_embed")
and controlnet_model.config.use_pos_embed is False
):
pos_embed = controlnet_model._get_pos_embed_from_transformer(transformer)
controlnet_model.pos_embed = pos_embed.to(controlnet_model.dtype).... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
text_encoder_3=text_encoder_3,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
tokenizer_3=tokenizer_3,
transformer=transformer,
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
else 128
) | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 256,
device: Optio... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
text_inputs = self.tokenizer_3(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tok... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
dtype = self.text_encoder_3.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
clip_tokenizers = [self.tokenizer, self.tokenizer_2]
clip_text_encoders = [self.text_encoder, self.text_encoder_2]
tokenizer = clip_tokenizers[clip_model_index]
text_encoder = clip_text_encoders[clip_model_index]
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_si... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = tokenizer.batch_decode(untruncated_ids[:,... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_e... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]],
prompt_3: Union[str, List[str]],
device: Optional[torch.device] = N... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
r""" | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers o... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, SD3LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and ... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
clip_model_index=0,
)
prompt_2_embed, pooled_prompt_2_... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
clip_prompt_embeds = torch.nn.functional.pad(
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
)
prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_pr... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# normalize str to list
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_2 = (
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
)
negative... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
negative_prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=None,
clip_model_index=0,
)
negative_prompt_2... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
negative_clip_prompt_embeds = torch.nn.functional.pad(
negative_clip_prompt_embeds,
(0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
)
negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def check_inputs(
self,
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=None,
negative_prompt_2=None,
negative_prompt_3=None,
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not ... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
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